Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

11-2013

Abstract

The recent years have seen an unprecedented boom of social network services, such as Twitter, which boasts over 200 million users. In such big social platforms, the influential users are ideal targets for viral marketing to potentially reach an audience of maximal size. Most proposed algorithms rely on the linkage structure of the respective underlying network to determine the information flow and hence indicate a users influence. From social interaction perspective, we built a model based on the dynamic user interactions constantly taking place on top of these linkage structures. In particular, in the Twitter setting we supposed a principle of balanced retweet reciprocity, and then formulated it to disclose the values of Twitter users. Our experiments on real Twitter data demonstrated that our proposed model presents different yet equally insightful ranking results. Besides, the conducted prediction test showed the correctness of our model.

Keywords

Twitter, User ranking, Retweet behavior

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Management and Analytics

Publication

Social Informatics: 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013: Proceedings

Volume

8238

First Page

227

Last Page

240

ISBN

9783319032603

Identifier

10.1007/978-3-319-03260-3_20

Publisher

Springer Verlag

City or Country

Cham

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1007/978-3-319-03260-3_20

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